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A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles
- Source :
- Frontiers in Genetics, Vol 12 (2021), Frontiers in Genetics
- Publication Year :
- 2021
- Publisher :
- Frontiers Media S.A., 2021.
-
Abstract
- Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.
- Subjects :
- Oncology
medicine.medical_specialty
Multivariate statistics
Framingham Risk Score
Proportional hazards model
business.industry
overall survival
Univariate
Disease
prediction
QH426-470
medicine.disease
Malignancy
Regression
risk score model
multiple myeloma
Internal medicine
medicine
Genetics
Molecular Medicine
prognosis
business
Genetics (clinical)
Multiple myeloma
Original Research
Subjects
Details
- Language :
- English
- ISSN :
- 16648021
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- Frontiers in Genetics
- Accession number :
- edsair.doi.dedup.....3cb85e438d7e40c3265b79bfda4e9273